A Complete Generalized Adjustment Criterion

Emilija Perkovic, Johannes Textor Utrecht University, Markus Kalisch, Marloes Maathuis
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:467-476, 2015.

Abstract

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-perkovic15a, title = {A Complete Generalized Adjustment Criterion}, author = {Perkovic, Emilija and University, Johannes Textor Utrecht and Kalisch, Markus and Maathuis, Marloes}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {467--476}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/perkovic15a/perkovic15a.pdf}, url = {https://proceedings.mlr.press/r13/perkovic15a.html}, abstract = {Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Complete Generalized Adjustment Criterion %A Emilija Perkovic %A Johannes Textor Utrecht University %A Markus Kalisch %A Marloes Maathuis %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-perkovic15a %I PMLR %P 467--476 %U https://proceedings.mlr.press/r13/perkovic15a.html %V R13 %X Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes. %Z Reissued by PMLR on 04 October 2026.
APA
Perkovic, E., University, J.T.U., Kalisch, M. & Maathuis, M.. (2015). A Complete Generalized Adjustment Criterion. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:467-476 Available from https://proceedings.mlr.press/r13/perkovic15a.html. Reissued by PMLR on 04 October 2026.

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